Here, we explore whether the authenticity of generated prompts affects the performance of detector.
In particular, we leverage a new term called prompt descriptiveness  [1]}, [2]}, [3]}, [4]} to evaluate the authenticity of prompt by computing the similarity between prompt embeddings and image embeddings.
The descriptiveness of one dataset is the mean of the descriptiveness of each text.
fig:des depicts the detection performance in terms of the authenticity of generated prompts.
We can see that the authenticity of the prompts greatly affects the performance of the detector.
For instance, when the descriptiveness is 0, i.e., no prompts are used as the input, the detector can only achieve the accuracy score of 0.621 on GLIDE MSCOCO, while the performance can be improved to 0.838 with the generated text with descriptiveness of 0.538.
In addition, it also can be concluded that we can easily obtain the generated prompts by BLIP that have good descriptiveness among all datasets and algorithms.
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